An entropy-based approach to automatic image segmentation of satellite images

TL;DR

This paper introduces an entropy-based method for automatic segmentation of satellite images from Google Earth, enabling automated ecological and urban monitoring with improved accuracy using color information.

Contribution

It presents a novel entropy-based segmentation technique specifically designed for color satellite images, enhancing object detection and regional classification.

Findings

Color images improve segmentation accuracy over grayscale.

The method effectively identifies aquatic, rural, and urban regions.

Segmentation accuracy is validated against ground truth data.

Abstract

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.